# Benchmark and Survey of Automated Machine Learning Frameworks

## TL;DR

This paper surveys current AutoML methods and benchmarks popular frameworks on 137 datasets, highlighting techniques to automate ML pipeline construction and reduce reliance on specialized data scientists.

## Contribution

It provides a comprehensive review of AutoML techniques and a benchmark comparison of leading frameworks on real-world datasets.

## Key findings

- AutoML frameworks vary significantly in performance.
- Certain frameworks excel in specific data domains.
- The survey identifies key techniques used across frameworks.

## Abstract

Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suits.